RADE Documentation

One of the aims of this project is to document our work to a professional standard. This month I spent some time working on two research papers, one on the HF RADE work, and one on the baseband FM (BBFM) work. I’m also working on a presentation of RADE technology aimed at Hams, that I will present at my local AREG club in March.

The RADE work has been moving pretty fast over the past 12 months, so I’ve found writing up the work beneficial to help me collect my thoughts and prepare for further development. It’s very new technology, and a lot of people are curious about how RADE technology works. This all means it takes some time and effort to explain. Another good reason to document this work is to get it out of my head and into a form that others can work with in future (another one of our grant aims).

We hope to publish the papers later this year. By writing the papers we also hope to promote the project and help communicate our work at a professional level to commercial companies who may be interested in integrating RADE technology into their products.

ML Equalisation

RADE is a mixture of classical DSP and ML signal processing. One interesting design choice is how to partition the design – which chunks of signal processing should use old school DSP, and which ML?

For RADE V1 we use ML to generate QAM symbols, but classical DSP to “equalise” these symbols at the receiver. You can think of equalisation as removing any phase and frequency offsets in the received signal – a bit like fine tuning a SSB receiver. In regular data modems equalisation stops the received modem constellation from rotating or spinning, so the correct bits can be demodulated.

For RADE V2 I am prototyping the use of ML for the equalisation. The curves below show the performance of various schemes I have tested so far, with RADE V1 (blue) as the control. The “loss” is a measure of distortion, lower is better. You can see the loss decrease as SNR increases, just as we would expect.

Now in practice RADE V1 actual requires about 3dB more SNR once we add the classical DSP equalisation so for a fair comparison it should be shifted 3dB to the right, making all of the curves within a few dB of each other. So the ML network is indeed performing the equalisation function, but too early to say if we have something that can outperform the classical DSP approach used in RADE V1.

The yellow curve is intriguing – its suggests that with the right network we can get better speech quality than RADE V1 at high SNRs.

More work required to work through the equalisation question and it’s very much R&D rather than Engineering, which makes the timeline for RADE V2 hard to predict. More next month…..

The FreeDV project recently came back from Orlando, FL, where it had a booth in the North Hall at Orlando HamCation. At the booth, we had a demo of the RADE work we’ve been working on, including two headsets (one playing “analog” audio as what would be transmitted through a microphone and the other playing receive audio after being transmitted and received using RADE). We additionally had internet access in the booth so we were able to show a live view of FreeDV Reporter:

Over the course of the weekend, we had significant interest in the FreeDV booth. On top of that, nearly everyone who listened to the RADE demo were impressed by the audio quality, especially when compared to the existing state of the art in digitial voice.

Additionally, Mooneer K6AQ gave a talk about RADE on Friday afternoon of the hamfest. For those who missed the talk or want to refer back to the slides, they’re available in PDF format below:

FreeDV will have a booth at the North Hall of Orlando HamCation this weekend (near the HamCation prize booth). If you happen to be in the area, come check us out! More information about HamCation can be found at https://www.hamcation.com/.

Additionally, Mooneer K6AQ will be giving a talk about/demoing FreeDV’s new RADE mode tomorrow (February 7) at 3:30pm in CS II (the set of popup tents in the back of the fairgrounds). Hope to see you there too!

This month was spent firming up the preview release that just came out. This involved fixing various bugs discovered by the RADE test team, such as various crashes that began occurring during the implementation of SNR and RADE text support (required for FreeDV Reporter to fully work with RADE). Audio quality was also improved and the transmission of the End Of Over (EOO) block was also made more reliable, ensuring that users reported received signals to FreeDV Reporter and PSK Reporter more often.

Speaking of PSK Reporter, this is the worldwide map of activity over the last 24 hours as a result of this work, indicating heavy interest in RADE and FreeDV more generally:

For February, the focus is going to be on promoting the work we’ve done as a project. We’ll be at Orlando HamCation in just a few days (North Hall, booth 119, as well as a talk given by me on Friday February 7th at 3:30pm)–hope to see you all there!

More information can be found in the commit history below:

• freedv-gui
• RADE

(Note that all commit logs above were generated with the following command line:)

git log --author="member@email" --after "Month 1, 2025" --before "Month 31, 2025" --all > commit.log

At the start of this month I did battle with the problem of SNR estimation on the RADE V1 signal. As I have mentioned previously, this had some challenges due to the lack of structure in the RADE constellation. After a few false starts I managed to get something viable running using the properties of the pilot symbols. The plot below shows the estimated against actual SNR for a range of channels. In the -5 to 10dB range (of most interest to us) it’s within 1dB for all but the MPP (fast fading) channel where the reported estimate reads a few dB lower than the actual (Note Es/No roughly the same as SNR for this example).

I’ve started work on RADE V2, where we hope to use lessons learned from RADE V1 to make some improvements and develop a “stable” waveform for general Ham use. This month I have made some progress in jointly optimising the PAPR and bandwidth of the RADE signals. For regulatory purposes, the bandwidth of signals like OFDM are often specified in terms of the “occupied bandwidth” (OBW) that contains 99% of the power. The figure below shows the spectrum of a 1000 symbols/s signal with a 1235 Hz 99% occupied bandwidth OBW in red.

Machine Learning Equalisation

Also for RADE V2, I have been prototyping ML based equalisation, and have obtained good results for some examples using the BER of QPSK symbols as a metric. The plot below shows the BER against Eb/No for the classical DSP (blue), and two candidate ML equalisers (red and green, distinguished by different loss functions). The channel had random phase offsets for every frame, which the equaliser had to correct. The three equalisers have more or less the same performance.

These results show the equalisation function can be performed ML networks, with equivalent performance to classical DSP.

Project Management

Quite a bit of admin this month, including time spent recruiting prospective new PLT members, updating budgets, and our annual report. Not as much fun as playing with machine learning, but necessary to keep the project running smoothly.

It was time to write our annual report for the ARDC who have kindly funded this project for the last two years. Writing this report underlined what a good year we had in 2024, some highlights:

• The development and Beta release of the Radio Autoencoder RADE V1 which is well on the way to meeting our goals of being competitive with SSB at high and low SNRs. Special thanks to Jean-Marc Valin for your mentoring and vision on this project!
• The BBFM project, paving the way for high quality speech on VHF/UHF land module radio (LMR) applications, in collaboration with Tibor Bece and George Karan.
• New data modes to support FreeDATA, in collaboration with Simon DJ2LS.
• The release of ezDV and continued maintenance of freedv-gui largely by Mooneer’s efforts.
• Peter Marks joining our Project Leadership Team. He’s already making a big impact – thanks Peter!